Associate Principal Engineer, AI Architect

Company Description

We’re Nagarro.

We are a Digital Product Engineering company that is scaling in a big way! We build products, services, and experiences that inspire, excite, and delight. We work at scale across all devices and digital mediums, and our people exist everywhere in the world (18000+ experts across 40 countries, to be exact). Our work culture is dynamic and non-hierarchical. We’re looking for great new colleagues. That’s where you come in!

Job Description

REQUIREMENTS:

• Total experience 9+ years.
• Should have experience in software engineering, with strong depth in Python.
• Must have experience to develop microservices and APIs (Python, FastAPI, Node.js) to embed AI and deep learning models into enterprise applications.
• Should have proven experience architecting and delivering production-grade Generative AI applications at scale.
• Should have strong system design skills across backend, frontend, and AI infrastructure layers.
• Should be able to architect and implement scalable ML, Generative AI, and Agentic AI solutions (LLMs, RAG, autonomous agents) aligned with business objectives.
• Must have good experience in Enterprise Architecture & Solution Design.
• Should be able to build robust end-to-end AI pipelines and integrate Vector databases (e.g., Pinecone, Weaviate, Milvus) for data processing and feature extraction.
• Must have experience to deploy secure, production-grade hybrid and cloud AI solutions (AWS/GCP/Azure) using modern CI/CD, AI-assisted code optimization, and strict access control.
• Should be able to leverage emerging AI research to continuously tune models, optimize codebases, and manage scalable compute resources.
• Must have experience defining technical strategy and influencing architecture across teams or pods.
• Should be able to mentor junior engineers, drive agile workflows (Jira), and partner with Product managers to define technical strategy.
• Maintain clear technical documentation and translate complex AI concepts for non-technical audiences.
• Should have strong grasp of AI Governance & Responsible AI, Multi-cloud AI platform architecture, AgentOps / GenAIOps, Knowledge Graphs & Semantic Layer Architecture.
• Should have hands-on experience with cloud platforms (AWS, Azure, or GCP) and distributed systems.
• Must have ability to translate ambiguous business problems into durable technical architectures.
• Should have excellent communication skills, with the ability to influence senior stakeholders and technical leadership.

RESPONSIBILITIES:

• Understanding the client’s business use cases and technical requirements and be able to convert them into technical design which elegantly meets the requirements.
• Own the architecture and technical vision for AI-powered, user-facing applications built with Python, React, and Generative AI.
• Design scalable, secure, and cost-efficient backend platforms for LLM inference, RAG pipelines, and agent-based orchestration.
• Define frontend architecture and UX patterns for AI-native applications, including conversational interfaces, copilots, and intelligent dashboards.
• Lead the design and implementation of complex GenAI workflows that combine LLMs, tools, APIs, structured data, and user context.
• Establish engineering standards and best practices for prompt design, model integration, evaluation, and observability.
• Drive GenAI platformisation—building reusable components, SDKs, and frameworks used across multiple teams or products.
• Partner with product, design, data, and business leaders to translate strategic objectives into scalable technical solutions.
• Review critical designs and codebases, unblock teams on complex technical challenges, and raise the overall engineering bar.
• Lead technical discovery and solutioning for high-impact initiatives, including client or executive-facing workshops when required.
• Ensure enterprise readiness: security, privacy, compliance, governance, and responsible AI practices.
• Use AI-assisted development tools (e.g., Copilot, Claude Code) to accelerate delivery while maintaining production-grade quality.
• Mapping decisions with requirements and be able to translate the same to developers.
• Identifying different solutions and being able to narrow down the best option that meets the client’s requirements.
• Defining guidelines and benchmarks for NFR considerations during project implementation
• Writing and reviewing design document explaining overall arc…